AI Conversation Logger MCP

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An intelligent MCP (Model Context Protocol) server designed specifically for AI assistants to automatically log and manage conversation history with developers.

🎯 Core Features

🤖 AI-Driven Logging - All content is determined and provided by the AI assistant
📝 Pure Save Mode - MCP only formats and stores, no content extraction or analysis
🔄 Designed for AI Retrospection - Log format optimized for AI to quickly understand project history
🏷️ Smart Organization - Auto-organize by project and date with tagging support
🔍 Powerful Search - Multi-dimensional search by keywords, files, tags, and time range
📊 Context Suggestions - Smart recommendations based on file associations

🚀 Quick Start

1. Install Dependencies

npm install

2. Build Project

npm run build

3. Configure Claude Code

Add MCP server configuration to Claude Code's config file (~/.claude.json):

{ "mcpServers": { "conversation-logger": { "command": "node", "args": ["/path/to/ai-conversation-logger-mcp/dist/index.js"] } } }

4. Restart Claude Code

Restart Claude Code to apply the configuration.

📚 API Tools

1. log_conversation - Core Logging Tool

Records every AI-user interaction with structured information:

interface LogConversationParams { userRequest: string; // User's original request + uploaded file descriptions aiTodoList: string[]; // AI's execution plan (list even for view-only tasks) aiSummary: string; // AI's operation summary (3-5 sentences) fileOperations?: string[]; // File operations in format: "action filepath - description" title?: string; // Conversation title (optional) tags?: string[]; // Tag array (optional) project?: string; // Project name (auto-detected if not provided) }

2. search_conversations - Search Tool

Search through conversation history with multiple filters:

interface SearchParams { keywords?: string[]; // Keyword search filePattern?: string; // File name pattern search days?: number; // Recent N days project?: string; // Project filter (defaults to current) tags?: string[]; // Tag filter limit?: number; // Result limit (default: 10) }

3. get_context_suggestions - Context Recommendations

Get relevant historical context based on current work:

interface ContextParams { currentInput: string; // Current user input currentFiles?: string[]; // Currently involved files project?: string; // Project filter (optional) }

📁 Storage Structure

Logs are stored in the project's ai-logs/ directory:

project-root/ ├── ai-logs/ │ ├── 2025-08-07.md # Daily conversation logs │ ├── 2025-08-06.md │ └── config.json # Project configuration ├── src/ └── ...

📝 Log Format

Each conversation is recorded with the following structure:

## [Timestamp] Title #tags ### 🗣️ User Request [Original user request] ### 📋 AI Execution Plan - [x] Completed task - [ ] Pending task ### 🤖 AI Summary [Summary of what was accomplished] ### 📂 File Operations - **Created** `path/to/file` - Purpose description - **Modified** `path/to/file` - What was changed - **Deleted** `path/to/file` - Reason for deletion ### 🏷️ Tags #module #technology #type

🎯 Usage Principles

When to Log

All conversations should be logged, including:

New feature development
Bug fixes (any size)
Code refactoring
Configuration changes
Code explanations and analysis
Technical Q&A
Code reviews
Any project-related dialogue

Key Points

AI-Driven Content - AI determines what information to log
Complete Context - Include all relevant details for future reference
Focus on "What" not "How" - Emphasize functionality over technical details
Consistent Format - Maintain standardized markdown structure

🛠️ Development

Development Mode

npm run dev

Run Tests

npm test

Code Linting

npm run lint npm run lint:fix

TypeScript Check

npm run type-check

🔧 Technical Stack

TypeScript - Type-safe development
MCP SDK - Model Context Protocol implementation
Node.js - Runtime environment
Jest - Testing framework

📄 License

MIT

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📮 Contact

For issues or suggestions, please open an issue on GitHub.

Deploy Server

security – no known vulnerabilities

license - not found

quality - confirmed to work

How are these scores calculated?

Related Resources

GitHub Repository

Need Help?

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